Can We Trust Inertial and Heart Rate Sensor Data from an APPLE Watch Device?

The use of wearable technologies for the monitoring of human movement has increased considerably in the past few years, with applications to sports and other physical activities. Energy expenditure, walking and running distance, step count, and heart rate are some of the metrics provided by such devices via smart phone applications. Most of the research studies have involved validating the accuracy and reliability of the activity monitors by using the post-processed data from the device. The aim of this preliminary study was to determine if we can trust sensor data obtained from an Apple watch. This study evaluated the pre-processed data from the watch through step counting and heart rate measurements, and compared it with known validated devices (in-house 9DOF inertial sensor and Polar H10TM). Repeated activities (walking, jogging, and stair climbing) of varying duration and intensity were conducted by participants of varying age and body mass index (BMI). Pearson correlation (r > 0.95) and Bland–Altman statistical analyses were applied to the data to determine the level of agreement between the validated devices and the watch. The sensors from the Apple watch counted steps and measured heart rate with a minimum error and performed as expected.

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Comparing Airborne Particulate Matter Intake Dose Assessment Models Using Low-Cost Portable Sensor Data

Sensors ◽

10.3390/s20051406 ◽

2020 ◽

Vol 20 (5) ◽

pp. 1406

Author(s):

Rok Novak ◽

David Kocman ◽

Johanna Amalia Robinson ◽

Tjaša Kanduč ◽

Dimosthenis Sarigiannis ◽

...

Keyword(s):

Heart Rate ◽

Particulate Matter ◽

Low Cost ◽

Minute Ventilation ◽

Dose Assessment ◽

Sensor Data ◽

Activity Data ◽

Time Activity ◽

Activity Monitors ◽

Coarse Information

Low-cost sensors can be used to improve the temporal and spatial resolution of an individual’s particulate matter (PM) intake dose assessment. In this work, personal activity monitors were used to measure heart rate (proxy for minute ventilation), and low-cost PM sensors were used to measure concentrations of PM. Intake dose was assessed as a product of PM concentration and minute ventilation, using four models with increasing complexity. The two models that use heart rate as a variable had the most consistent results and showed a good response to variations in PM concentrations and heart rate. On the other hand, the two models using generalized population data of minute ventilation expectably yielded more coarse information on the intake dose. Aggregated weekly intake doses did not vary significantly between the models (6–22%). Propagation of uncertainty was assessed for each model, however, differences in their underlying assumptions made them incomparable. The most complex minute ventilation model, with heart rate as a variable, has shown slightly lower uncertainty than the model using fewer variables. Similarly, among the non-heart rate models, the one using real-time activity data has less uncertainty. Minute ventilation models contribute the most to the overall intake dose model uncertainty, followed closely by the low-cost personal activity monitors. The lack of a common methodology to assess the intake dose and quantifying related uncertainties is evident and should be a subject of further research.

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Application and Validation of Activity Monitors’ Epoch Lengths and Placement Sites for Physical Activity Assessment in Exergaming

Journal of Clinical Medicine ◽

10.3390/jcm7090268 ◽

2018 ◽

Vol 7 (9) ◽

pp. 268 ◽

Cited By ~ 14

Author(s):

Jungyun Hwang ◽

Austin Fernandez ◽

Amy Lu

Keyword(s):

Physical Activity ◽

Heart Rate ◽

Intraclass Correlation ◽

Activity Levels ◽

Physical Activity Intensity ◽

Activity Monitors ◽

Epoch Length ◽

Activity Intensity ◽

Physical Activity Levels ◽

Rate Sensor

We assessed the agreement of two ActiGraph activity monitors (wGT3X vs. GT9X) placed at the hip and the wrist and determined an appropriate epoch length for physical activity levels in an exergaming setting. Forty-seven young adults played a 30-min exergame while wearing wGT3X and GT9X on both hip and wrist placement sites and a heart rate sensor below the chest. Intraclass correlation coefficient indicated that intermonitor agreement in steps and activity counts was excellent on the hip and good on the wrist. Bland-Altman plots indicated good intermonitor agreement in the steps and activity counts on both placement sites but a significant intermonitor difference was detected in steps on the wrist. Time spent in sedentary and physical activity intensity levels varied across six epoch lengths and depended on the placement sites, whereas time spent from a 1-s epoch of the hip-worn monitors most accurately matched the relative exercise intensity by heart rate. Hip placement site was associated with better step-counting accuracy for both activity monitors and more valid estimation of physical activity levels. A 1-s epoch was the most appropriate epoch length to detect short bursts of intense physical activity and may be the best choice for data processing and analysis in exergaming studies examining intermittent physical activities.

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Comprehensive comparison of Apple Watch and Fitbit monitors in a free-living setting

PLoS ONE ◽

10.1371/journal.pone.0251975 ◽

2021 ◽

Vol 16 (5) ◽

pp. e0251975

Author(s):

Yang Bai ◽

Connie Tompkins ◽

Nancy Gell ◽

Dakota Dione ◽

Tao Zhang ◽

...

Keyword(s):

Heart Rate ◽

Pearson Correlation ◽

Living Condition ◽

Equivalence Testing ◽

Free Living ◽

Activity Monitors ◽

Step Counts ◽

Comprehensive Comparison ◽

Rate Correlation ◽

Absolute Percent Error

Objectives The aim of this study was to evaluate the accuracy of three consumer-based activity monitors, Fitbit Charge 2, Fitbit Alta, and the Apple Watch 2, all worn on the wrist, in estimating step counts, moderate-to-vigorous minutes (MVPA), and heart rate in a free-living setting. Methods Forty-eight participants (31 females, 17 males; ages 18–59) were asked to wear the three consumer-based monitors mentioned above on the wrist, concurrently with a Yamax pedometer as the criterion for step count, an ActiGraph GT3X+ (ActiGraph) for MVPA, and a Polar H7 chest strap for heart rate. Participants wore the monitors for a 24-hour free-living condition without changing their usual active routine. MVPA was calculated in bouts of ≥10 minutes. Pearson correlation, mean absolute percent error (MAPE), and equivalence testing were used to evaluate the measurement agreement. Results The average step counts recorded for each device were as follows: 11,734 (Charge2), 11,922 (Alta), 11,550 (Apple2), and 10,906 (Yamax). The correlations in steps for the above monitors ranged from 0.84 to 0.95 and MAPE ranged from 17.1% to 35.5%. For MVPA minutes, the average were 76.3 (Charge2), 63.3 (Alta), 49.5 (Apple2), and 47.8 (ActiGraph) minutes accumulated in bouts of 10 or greater minutes. The correlation from MVPA estimation for above monitors were 0.77, 0.91, and 0.66. MAPE from MVPA estimation ranged from 44.7% to 55.4% compared to ActiGraph. For heart rate, correlation for Charge2 and Apple2 was higher for sedentary behavior and lower for MVPA. The MAPE ranged from 4% to 16%. Conclusion All three consumer monitors estimated step counts fairly accurately, and both the Charge2 and Apple2 reported reasonable heart rate estimation. However, all monitors substantially underestimated MVPA in free-living settings.

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Linkage between machine power utilization and ergonomics, with reference to reducing exertions at work

Work ◽

10.3233/wor-203345 ◽

2020 ◽

Vol 67 (4) ◽

pp. 949-957

Author(s):

Abdollah Hayati ◽

Afshin Marzban

Keyword(s):

Heart Rate ◽

Energy Expenditure ◽

Unit Area ◽

Pearson Correlation ◽

Field Capacity ◽

Contributing Factors ◽

Factors Affecting ◽

Human Energy ◽

Rate Sensor ◽

Working Heart

BACKGROUND: Despite mechanization development, leafy vegetable cultivation (LVC), as a labor-intensive activity in both developed and developing countries, still suffers from heavy physical activities. OBJECTIVE: The present study evaluated the human physiological strains of LVC’s workers to identify relationships among contributing factors affecting human physiological strains. METHODS: Thirty male workers were included in this study. Working heart rate (HR) was measured using a heart rate sensor during various operations. The time taken to treat a known area was measured using a stopwatch to calculate work speed (or field capacity (FC)) for each operation. Pearson correlation coefficient and linear regression were used to investigate the relationships among HR, heart rate ratio, FC and mechanization status (MS), and human energy expenditure rate and total energy expenditure per unit area. RESULTS: The highest HR was at seedbed preparing (120.1 beats/min) and lowest at manual harvesting (87.8 beats/min). Manual hoe-used operations (seedbed preparing, manure application and irrigating) were demonstrated as the critical operations concerning physiological strains. The operations performed by machine power corresponded to a high FC. CONCLUSIONS: Variables influencing the area treating speed (i.e. MS and FC) are negatively linked to the human energy consumed per unit area and variable changed in time unit (i.e. HR) was positively linked to the human energy expenditure speed.

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Variable agreement between wearable heart rate monitors during exercise in cystic fibrosis

ERJ Open Research ◽

10.1183/23120541.00006-2019 ◽

2019 ◽

Vol 5 (4) ◽

pp. 00006-2019 ◽

Cited By ~ 2

Author(s):

Madeline Gaynor ◽

Abbey Sawyer ◽

Sue Jenkins ◽

Jamie Wood

Keyword(s):

Cystic Fibrosis ◽

Heart Rate ◽

Pulse Oximeter ◽

Cycle Ergometer ◽

Heart Rate Monitors ◽

Activity Monitors ◽

Interval Exercise ◽

Bland Altman Method ◽

Rate Sensor

In people with cystic fibrosis (CF), greater cardiorespiratory fitness is associated with improved survival and quality of life. Wearable activity monitors are a popular method of monitoring exercise, with measures of heart rate used to indicate exercise intensity. We assessed the agreement of heart rate recordings obtained using the Fitbit Charge HR™, Polar® H7 heart rate sensor and Masimo SET® Rad-5v pulse oximeter with the three-lead ECG during continuous and interval exercise.Adults with CF completed two exercise sessions, of 15-min duration per session, on a cycle ergometer while wearing the previously mentioned monitors. Firstly, participants cycled at 30% of estimated peak workload (Wpeak). Secondly, participants cycled at 1-min intervals at 60% of Wpeak interspersed with 2 min of unloaded cycling. Heart rate readings on all devices were recorded at minute intervals and their agreement was analysed using the Bland–Altman method.The Polar® H7 heart rate sensor had the best agreement with three-lead ECG, with a bias of 0±1 bpm during both continuous and interval exercise. The Masimo SET® Rad-5v pulse oximeter had good agreement, with a bias of 1±7 bpm and 1±5 bpm during continuous and interval exercise, respectively. The Fitbit Charge HR™ demonstrated less agreement, with a bias of 9±17 bpm and 5±13 bpm during continuous and interval exercise, respectively.The Fitbit Charge HR™ is not recommended for assessing heart rate during exercise in adults with CF. Findings support the use of the Polar® H7 for accurate heart rate monitoring.

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Methods for Improving Foot Displacement Measurements Calculated from Inertial Sensors

Biomedical Engineering and Information Systems - Advances in Bioinformatics and Biomedical Engineering ◽

10.4018/978-1-61692-004-3.ch005 ◽

2011 ◽

pp. 93-116 ◽

Cited By ~ 2

Author(s):

Edgar Charry ◽

Daniel T.H. Lai

Keyword(s):

Measurement System ◽

Inertial Sensors ◽

Inertial Sensor ◽

Human Movement ◽

Sensor Data ◽

Band Pass Filter ◽

Pass Filter ◽

Band Pass ◽

Sensor Errors ◽

Displacement Measurements

The use of inertial sensors to measure human movement has recently gained momentum with the advent of low cost micro-electro-mechanical systems (MEMS) technology. These sensors comprise accelerometer and gyroscopes which measure accelerations and angular velocities respectively. Secondary quantities such as displacement can be obtained by integration of these quantities, a method which presents challenging issues due to the problem of accumulative sensor errors. This chapter investigates the spectral evaluation of individual sensor errors and looks at the effectiveness of minimizing these errors using static digital filters. The primary focus is on the derivation of foot displacement data from inertial sensor measurements. The importance of foot, in particular toe displacement measurements is evident in the context of tripping and falling which are serious health concerns for the elderly. The Minimum Toe Clearance (MTC) as an important gait variable for falls-risk prediction and assessment, and therefore the measurement variable of interest. A brief sketch of the current devices employing accelerometers and gyroscopes is presented, highlighting the problems and difficulties reported in literature to achieve good precision. These have been mainly due to the presence of sensor errors and the error accumulative process employed in obtaining displacement measurements. The investigation first proceeds to identify the location of these sensor errors in the frequency domain using the Fast Fourier Transform (FFT) on raw inertial sensor data. The frequency content of velocity and displacement measurements obtained from integrating the inertial data using a well known strap-down method is then explored. These investigations revealed that large sensor errors occurred mainly in the low frequency spectrum while white noise exists in all frequency spectra. The efficacy of employing a band-pass filter to remove a large portion of these errors and their effect on the derived displacements is elaborated on. The cross-correlation of the FFT power spectra from a highly accurate optical measurement system and processed sensor data is used as a metric to evaluate the performance of the band-pass filter at several stages of the processing stage. The motivation is that a more fundamental method would require less computational demand and could lead to more efficient implementations in low-power and systems with limited resources, so that portable sensor based motion measurement system would provide a good degree of measurement accuracy.

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THE IMPLEMENTATION OF HEART RATE SENSOR AND MOTION SENSORS BASED ON INTERNET OF THINGS FOR ATLETE PERFORMANCE MONITORING

Kursor ◽

10.28961/kursor.v10i1.208 ◽

2019 ◽

Vol 10 (1) ◽

pp. 196

Author(s):

Muhammad Aksa Hidayat ◽

Sritrusta Sukaridhoto ◽

Achmad Basuki ◽

Udin Harun Al Rasyid ◽

Ika Fadhila Aryanti ◽

...

Keyword(s):

Heart Rate ◽

Internet Of Things ◽

Motion Capture ◽

Performance Monitoring ◽

Sensor Data ◽

Raspberry Pi ◽

Motion Sensors ◽

Web Based ◽

Rate Sensor ◽

The Internet Of Things

Indonesian achievements in the ASEAN Games continued to decline in achievement starting in 1962 with the acquisition of 51 medals and up to 2014 with the acquisition of 20 medals. The decline in achievement was due to the lack of athletic resources due to the absence of media that could record athletes' abilities in the field. Can record the athlete's performance before running, running and after running using the Heart Rate sensor and Motion Capture sensor. The results of the sensor recording will be stored in the database. This system applies the Internet of Things (IoT) concept, using raspberry pi, Arduino microcontroller, T34 polar heart rate sensor to capture and send heartbeat to receivers, gyro-based motion-capture sensors that named wear notch where this sensor serves to capture the movement of athletes, sensors communicate with the system using 4G connectivity, use MQTT as edge computing which acts as a communication medium from sensors to databases, Maria DB and influx DB as accumulation which plays a role in storing heart rate and athlete's movements that have been recorded by sensors, athlete performance monitoring platform with a heart rate sensor and athlete's motion capture is a web-based application that collaborates all processes from the sensor to the system. Sensor heart rate recording results are categorized good because the error margin is only 0.4%. Wearnotch sensor data can be stored in the database, and athletic data can be recorded before sports, while sports, and after sports in real-time

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Detection of Frequent Lane-Change Behavior Using Smart Phone Inertial Sensor Data

CICTP 2020 ◽

10.1061/9780784483053.034 ◽

2020 ◽

Author(s):

Yuqin Zhang ◽

Zhigang Xu ◽

Bin Tian ◽

Shangrong Li ◽

Zijun Jiang

Keyword(s):

Inertial Sensor ◽

Smart Phone ◽

Sensor Data ◽

Lane Change ◽

Change Behavior

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Template-Based Recognition of Human Locomotion in IMU Sensor Data Using Dynamic Time Warping

Sensors ◽

10.3390/s21082601 ◽

2021 ◽

Vol 21 (8) ◽

pp. 2601

Author(s):

Kim S. Sczuka ◽

Marc Schneider ◽

Alan K. Bourke ◽

Sabato Mellone ◽

Ngaire Kerse ◽

...

Keyword(s):

Dynamic Time Warping ◽

Inertial Sensor ◽

Vigorous Physical Activity ◽

Human Locomotion ◽

Recognition Algorithm ◽

Stair Climbing ◽

Sensor Data ◽

Time Warping ◽

Lower Back ◽

Dynamic Time

Increased levels of light, moderate and vigorous physical activity (PA) are positively associated with health benefits. Therefore, sensor-based human activity recognition can identify different types and levels of PA. In this paper, we propose a two-layer locomotion recognition method using dynamic time warping applied to inertial sensor data. Based on a video-validated dataset (ADAPT), which included inertial sensor data recorded at the lower back (L5 position) during an unsupervised task-based free-living protocol, the recognition algorithm was developed, validated and tested. As a first step, we focused on the identification of locomotion activities walking, ascending and descending stairs. These activities are difficult to differentiate due to a high similarity. The results showed that walking could be recognized with a sensitivity of 88% and a specificity of 89%. Specificity for stair climbing was higher compared to walking, but sensitivity was noticeably decreased. In most cases of misclassification, stair climbing was falsely detected as walking, with only 0.2–5% not assigned to any of the chosen types of locomotion. Our results demonstrate a promising approach to recognize and differentiate human locomotion within a variety of daily activities.

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Relationship between Invasive and Non-Invasive Hemodynamic Measures in Experimental Pulmonary Hypertension

Current Respiratory Medicine Reviews ◽

10.2174/1573398x16666200516180118 ◽

2020 ◽

Vol 16 (1) ◽

pp. 47-53

Author(s):

Vicente Benavides-Córdoba ◽

Mauricio Palacios Gómez

Keyword(s):

Heart Rate ◽

Pulmonary Hypertension ◽

Ejection Fraction ◽

Right Ventricle ◽

Correlation Coefficient ◽

Pearson Correlation ◽

Systolic Pressure ◽

Control Group ◽

Pearson Correlation Coefficient ◽

Non Invasive

Introduction: Animal models have been used to understand the pathophysiology of pulmonary hypertension, to describe the mechanisms of action and to evaluate promising active ingredients. The monocrotaline-induced pulmonary hypertension model is the most used animal model. In this model, invasive and non-invasive hemodynamic variables that resemble human measurements have been used. Aim: To define if non-invasive variables can predict hemodynamic measures in the monocrotaline-induced pulmonary hypertension model. Materials and Methods: Twenty 6-week old male Wistar rats weighing between 250-300g from the bioterium of the Universidad del Valle (Cali - Colombia) were used in order to establish that the relationships between invasive and non-invasive variables are sustained in different conditions (healthy, hypertrophy and treated). The animals were organized into three groups, a control group who was given 0.9% saline solution subcutaneously (sc), a group with pulmonary hypertension induced with a single subcutaneous dose of Monocrotaline 30 mg/kg, and a group with pulmonary hypertension with 30 mg/kg of monocrotaline treated with Sildenafil. Right ventricle ejection fraction, heart rate, right ventricle systolic pressure and the extent of hypertrophy were measured. The functional relation between any two variables was evaluated by the Pearson correlation coefficient. Results: It was found that all correlations were statistically significant (p <0.01). The strongest correlation was the inverse one between the RVEF and the Fulton index (r = -0.82). The Fulton index also had a strong correlation with the RVSP (r = 0.79). The Pearson correlation coefficient between the RVEF and the RVSP was -0.81, meaning that the higher the systolic pressure in the right ventricle, the lower the ejection fraction value. Heart rate was significantly correlated to the other three variables studied, although with relatively low correlation. Conclusion: The correlations obtained in this study indicate that the parameters evaluated in the research related to experimental pulmonary hypertension correlate adequately and that the measurements that are currently made are adequate and consistent with each other, that is, they have good predictive capacity.

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